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Dependence-Robust Inference Using Resampled Statistics

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Document pages: 37 pages

Abstract: We develop inference procedures robust to general forms of weak dependence.The procedures use test statistics constructed by resampling data in a mannerthat does not depend on the unknown correlation structure of the data. We provethat the statistics are asymptotically normal under the weak requirement thatthe target parameter can be consistently estimated at the parametric rate. Thisholds for regular estimators under many well-known forms of weak dependence andjustifies the claim of dependence-robustness. We consider applications tosettings with unknown or complicated forms of dependence, with various forms ofnetwork dependence as leading examples. We develop tests for both momentequalities and inequalities.

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